Wombat Facial Recognition Mobile App
Budget: $5,000 – $10,000 AUD
Wombat Biometric Recognition App – Project Brief
1. Project Vision
We’re building a world-first iOS app that uses AI-driven facial recognition to identify individual wombats in the wild, captured from iPhone photos taken at a distance. Unlike pets or zoo animals, wild wombats require a system that can identify them based on subtle, consistent markers across their lifespan, even from juvenile to adult.
This app will support wildlife monitoring, research, and conservation, laying the groundwork for future applications across other species.
2. Project Scope & Goals
Develop an iPhone app to identify individual wild wombats using biometric markers.
Photos will be captured remotely (no close-up nose prints).
The system must recognise individuals across time, accounting for aging and environmental variations.
Use AI/ML models to match images against a database of known wombats.
3. Technical & Biometric Challenges
Wild wombats are visually similar, requiring the AI to focus on extremely subtle traits. Challenges include:
Low-res images: Captured from varying distances and angles.
Aging: Juvenile photos must match adult appearances.
Subtle distinguishing markers:
Skin around the eyes
Nose colour and shape
For texture and direction
Curvature of the skull
Ear placement
Overall facial symmetry
These characteristics will require custom-trained computer vision models, potentially using transfer learning on top of pre-trained animal detection frameworks.
4. Features & Deliverables
Core Features
iOS app with camera integration
On-device photo capture and upload
Biometric AI model for facial recognition
Matching engine to identify known individuals
Image and wombat profile database (backend)
Optional / Future Features
Android version
GPS tagging or map integration
Conservation reporting features
Expansion to other animal species
Deliverables
Functional iOS prototype
Trained AI model or training pipeline
Backend (cloud-based or scalable database)
Full documentation and source code handover
5. Tools, Tech & Platform Ideas
We’re open to using proven AI/ML platforms and frameworks:
Mobile AI: CoreML, TensorFlow Lite, Google ML Kit
Cloud/Backend: AWS/GCP/Azure (for model training + data storage)
Custom Vision: TensorFlow Object Detection API, YOLOv8, or custom CNNs
Inspiration: PetNow.io (nose print recognition for dogs)
You may also explore:
Wildlife Insights
Google’s AI for the Planet initiative
6. Dataset
We have an initial photo library of 25 wombats, each with known IDs. This dataset will be used to test the feasibility of biometric recognition and train the prototype model.
7. Ideal Developer/Team Profile
We’re looking for developers, teams, or agencies with experience in:
Wildlife or pet biometric recognition (e.g., dogs, horses, zoo animals)
Facial recognition, computer vision, and machine learning
iOS development (Swift, SwiftUI)
On-device AI inference (CoreML, TensorFlow Lite)
Training models in low-data environments
Potential sources:
AI/ML consultancies (e.g. Latent Logic, Wildlife AI labs)
Research groups (AI + zoology crossover work)
GitHub/LinkedIn contributors to similar open-source tools
8. Timeline & Budget
Start: Ideally within 2–4 weeks
Timeline: Flexible based on scope and available talent
Budget: Open to quotes — please include time estimates and pricing tiers
9. To Respond, Please Include:
Relevant experience or portfolio links
Tools and frameworks you would use
Initial thoughts or suggestions to improve the approach
Any clarifying questions about the dataset, goals, or features
Why This Matters
This project has the potential to be a global first in wildlife recognition—advancing conservation technology and helping researchers monitor animals without tagging or invasive methods. If successful, it could serve as a template for identifying other species under similar conditions.
1. Project Vision
We’re building a world-first iOS app that uses AI-driven facial recognition to identify individual wombats in the wild, captured from iPhone photos taken at a distance. Unlike pets or zoo animals, wild wombats require a system that can identify them based on subtle, consistent markers across their lifespan, even from juvenile to adult.
This app will support wildlife monitoring, research, and conservation, laying the groundwork for future applications across other species.
2. Project Scope & Goals
Develop an iPhone app to identify individual wild wombats using biometric markers.
Photos will be captured remotely (no close-up nose prints).
The system must recognise individuals across time, accounting for aging and environmental variations.
Use AI/ML models to match images against a database of known wombats.
3. Technical & Biometric Challenges
Wild wombats are visually similar, requiring the AI to focus on extremely subtle traits. Challenges include:
Low-res images: Captured from varying distances and angles.
Aging: Juvenile photos must match adult appearances.
Subtle distinguishing markers:
Skin around the eyes
Nose colour and shape
For texture and direction
Curvature of the skull
Ear placement
Overall facial symmetry
These characteristics will require custom-trained computer vision models, potentially using transfer learning on top of pre-trained animal detection frameworks.
4. Features & Deliverables
Core Features
iOS app with camera integration
On-device photo capture and upload
Biometric AI model for facial recognition
Matching engine to identify known individuals
Image and wombat profile database (backend)
Optional / Future Features
Android version
GPS tagging or map integration
Conservation reporting features
Expansion to other animal species
Deliverables
Functional iOS prototype
Trained AI model or training pipeline
Backend (cloud-based or scalable database)
Full documentation and source code handover
5. Tools, Tech & Platform Ideas
We’re open to using proven AI/ML platforms and frameworks:
Mobile AI: CoreML, TensorFlow Lite, Google ML Kit
Cloud/Backend: AWS/GCP/Azure (for model training + data storage)
Custom Vision: TensorFlow Object Detection API, YOLOv8, or custom CNNs
Inspiration: PetNow.io (nose print recognition for dogs)
You may also explore:
Wildlife Insights
Google’s AI for the Planet initiative
6. Dataset
We have an initial photo library of 25 wombats, each with known IDs. This dataset will be used to test the feasibility of biometric recognition and train the prototype model.
7. Ideal Developer/Team Profile
We’re looking for developers, teams, or agencies with experience in:
Wildlife or pet biometric recognition (e.g., dogs, horses, zoo animals)
Facial recognition, computer vision, and machine learning
iOS development (Swift, SwiftUI)
On-device AI inference (CoreML, TensorFlow Lite)
Training models in low-data environments
Potential sources:
AI/ML consultancies (e.g. Latent Logic, Wildlife AI labs)
Research groups (AI + zoology crossover work)
GitHub/LinkedIn contributors to similar open-source tools
8. Timeline & Budget
Start: Ideally within 2–4 weeks
Timeline: Flexible based on scope and available talent
Budget: Open to quotes — please include time estimates and pricing tiers
9. To Respond, Please Include:
Relevant experience or portfolio links
Tools and frameworks you would use
Initial thoughts or suggestions to improve the approach
Any clarifying questions about the dataset, goals, or features
Why This Matters
This project has the potential to be a global first in wildlife recognition—advancing conservation technology and helping researchers monitor animals without tagging or invasive methods. If successful, it could serve as a template for identifying other species under similar conditions.